Dispersal dynamics in food webs

Studies of food webs suggest that limited nonrandom dispersal can play an important role in structuring food webs. It is not clear, however, whether density-dependent dispersal fits empirical patterns of food webs better than density-independent dispersal. Here, we study a spatially distributed food web, using a series of population-dispersal models that contrast density-independent and density-dependent dispersal in landscapes where sampled sites are either homogeneously or heterogeneously distributed. These models are fitted to empirical data, allowing us to infer mechanisms that are consistent with the data. Our results show that models with density-dependent dispersal fit the α, β, and γ tritrophic richness observed in empirical data best. Our results also show that density-dependent dispersal leads to a critical distance threshold beyond which site similarity (i.e., β tritrophic richness) starts to decrease much faster. Such a threshold can also be detected in the empirical data. In contrast, models with density-independent dispersal do not predict such a threshold. Moreover, preferential dispersal from more centrally located sites to peripheral sites does not provide a better fit to empirical data when compared with symmetric dispersal between sites. Our results suggest that nonrandom dispersal in heterogeneous landscapes is an important driver that shapes local and regional richness (i.e., α and γ tritrophic richness, respectively) as well as the distance-decay relationship (i.e., β tritrophic richness) in food webs.

Dynamics of the nasal microbiota in infancy: A prospective cohort study.

BACKGROUND Understanding the composition and dynamics of the upper respiratory tract microbiota in healthy infants is a prerequisite to investigate the role of the microbiota in patients with respiratory diseases. This is especially true in early life, when the immune system is in development. OBJECTIVE We sought to describe the dynamics of the upper respiratory tract microbiota in healthy infants within the first year of life. METHODS After exclusion of low-quality samples, microbiota characterization was performed by using 16S rDNA pyrosequencing of 872 nasal swabs collected biweekly from 47 unselected infants. RESULTS Bacterial density increased and diversity decreased within the first year of life (R(2) = 0.95 and 0.73, respectively). A distinct profile for the first 3 months of life was found with increased relative abundances of Staphlyococcaceae and Corynebacteriaceae (exponential decay: R(2) = 0.94 and 0.96, respectively). In addition, relative bacterial abundance and composition differed significantly from summer to winter months. The individual composition of the microbiota changed with increasing time intervals between samples and was best modeled by an exponential function (R(2) = 0.97). Within-subject dissimilarity in a 2-week time interval was consistently lower than that between subjects, indicating a personalized microbiota. CONCLUSION This study reveals age and seasonality as major factors driving the composition of the nasal microbiota within the first year of life. A subject's microbiota is personalized but dynamic throughout the first year. These data are indispensable to interpretation of cross-sectional studies and investigation of the role of the microbiota in both healthy subjects and patients with respiratory diseases. They might also serve as a baseline for future intervention studies.